Tree-based data exploration and data-driven protocol

ABSTRACT

A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119(e)from U.S. Provisional Application Ser. No. 62/741,985 filed Oct. 5,2018, the contents of which are hereby incorporated by reference intheir entirety.

BACKGROUND

Acute and chronic cardiovascular conditions reduce quality of life andlife expectancy. A variety of treatment modalities have been developedfor treatment of the heart in such conditions, ranging frompharmaceuticals to mechanical devices and transplantation. Ventricularassist devices (VADs), such as heart pump systems and catheter systems,are often used in treatment of the heart to provide hemodynamic supportand facilitate recovery. Some heart pump systems are percutaneouslyinserted into the heart and can run in parallel with the native heart tosupplement cardiac output. Such heart pump systems include the Impella®family of devices by Abiomed, Inc. of Danvers, Mass.

At present, the choice of a treatment plan using a VAD for patients withcardiovascular conditions is provided to a physician by a VADcontroller, and is largely based on statistics of prior success withusing the VAD for treating patients having similar conditions.Traditional data analytics are able to analyze a survival rateassociated with the use of a VAD based on a single factor, e.g. apatient's gender or age. With the evolution of the types of myocardialconditions that a patient is susceptible to, treatment plans that arenot streamlined to take into account additional factors that affect apatient's condition may deteriorate the patient's condition throughtreatment of the patient using a sub-optimal VAD.

SUMMARY

The methods and systems described herein use a tree-based predictivemodel to provide a physician with a treatment recommendation that isoptimized to the patient. The method begins by determining, using aprocessor, a first treatment recommendation based on a combination ofselected patient demographics obtained from a patient data repositorythat are applicable to the patient, and operational parameters of aplurality of ventricular assist devices (VADs) suitable for treating thepatient, the first treatment recommendation having a first survival rateand comprising the use of a first VAD. The method then progresses toobtain a first signal from the first VAD through use of the VAD to treatthe patient. The first signal is obtained from a controller incommunication with the first VAD. The processor then determines a secondtreatment recommendation based on the first signal and the firsttreatment recommendation, the second treatment recommendation having asecond survival rate. The processor then determines if the secondsurvival rate is higher than the first survival rate, and, if so,provides the second treatment recommendation to the physician.

In some implementations, the method further comprises informing thephysician to continue using the first VAD to treat the patient if thesecond survival rate is not higher than the first survival rate. Thus ifthe processor determines that the second survival rate is equal to orlower than the first, the treatment of the patient using the first VADis continued. In certain implementations, each VAD comprises at leastone sensor for providing the first signal to the controller. The sensormay be any input transducer that is configured to convert patient datainto electrical signals. In some implementation, the first signalcomprises information that relates to the patient's vitals, such as, atleast one of: Mean Arterial Pressure (MAP), Left Ventricular Pressure(LVP), Left Ventricular End-Diastolic Pressure (LVEDP), PulmonaryArterial Wedge Pressure (PAWP), Pulmonary Capillary Wedge Pressure(PCWP), Pulmonary Artery Occlusion Pressure (PAOP), for example.

In certain implementations, if the second survival rate is determined tobe higher than the first survival rate, the method further comprisestreating the patient with the second treatment recommendation. Thesecond treatment recommendation may comprise the use of at least one ofthe following for treating the patient: the first VAD, a second VAD, andno VAD. In some implementations, the second treatment recommendation maybuild upon the first treatment recommendation in that an additional VADto use with the first VAD may be recommended. In certainimplementations, the VAD comprises at least one of: an Impella® pump, anExtracorporeal Membrane Oxygenation (ECMO) pump, a balloon pump, and aSwan-Ganz catheter. The Impella® pump comprises any one of: Impella 2.5®pump, an Impella 5.0® pump, an Impella CP® pump, an Impella RP® pump andan Impella LD® pump. The aforementioned methods are used to for treatinga patient in cardiogenic shock.

In some implementations, the first treatment recommendation isdetermined by a prediction model executed by the processor. In certainimplementations, the prediction model is based on a machine learningalgorithm comprising any one of: a bagging and random forest algorithm,a logistic regression algorithm, a classification decision treealgorithm, a deep learning algorithm, a naïve Bayes algorithm, and asupport vector machines algorithm. In some implementations, theprediction model uses patient demographics that are applicable to thepatient in its calculations. Such demographics include gender, age,region, duration of support, indication for use and insertion site. Byusing demographics that are patient specific, the resulting treatmentrecommendation provided to the patient is better suited to each patient,thus improving treatment efficacy.

In certain implementations, the processor may display the survival ratefor each available VAD; and identifying the VAD with the highestsurvival rate. Additionally, the processor may display the combinationof the selected patient demographics used for determining the survivalrate using a branched-tree representation. Such feature combinationtrees provide the physician with a visualization of the features thathave an influence on the recommended VAD (and associated survival rate)for treating the patient. In relation to the present disclosure, thesurvival rate comprises a probability of survival of a patient belongingto the combination of selected patient demographics when treated with aVAD.

In some implementations, the patient data repository comprises a AcuteMyocardial Infarction Cardiogenic Shock (AMICS) database or a High-RiskPercutaneous Coronary Interventions (High-Risk PCI) database.

According to a second embodiment of the present disclosure, the methodsand systems obtain data from a patient repository in which the datastored in the repository according to patient demographics. The methoduses a processor to obtain data from the patient repository. Theprocessor then determines at least one ventricular assist device (VAD)suitable for treating patient. The processor then determined, using aprediction model, a survival rate for each suitable VAD based on datafrom the patient data repository for a combination of selected patientdemographics applicable to the patient. The processor then provides to acontroller a recommended first VAD associated with the highest survivalrate. The physician then uses the recommended first VAD to treat thepatient.

In some implementations, the method further provides the physician withthe survival rate for each suitable VAD for all combinations of theselected patient demographics applicable to the patient. In certainimplementations, the processor also provides the physician with asurvival rate for not using a VAD for each combination of the selectedpatient demographics applicable to the patient. In certainimplementations, the first VAD may comprise at least one of: an Impella®pump, an Extracorporeal Membrane Oxygenation (ECMO) pump, a balloonpump, and a Swan-Ganz catheter. The Impella® pump may comprise any oneof: Impella 2.5® pump, an Impella 5.0® pump, an Impella CP® pump, anImpella RP® pump and an Impella LD® pump. The patient demographics maycomprise: age, gender, region, year of implantation, support device,duration of support, insertion site and ejection fraction.

In some implementations, the prediction model uses a machine learningalgorithm to determine the survival rate. The machine learning algorithmmay comprise any one of: a bagging and random forest algorithm, alogistic regression algorithm, a classification decision tree algorithm,a deep learning algorithm, a naïve Bayes algorithm, and a support vectormachines algorithm In certain implementations, the combination of theselected patient demographics follows a tree-model. The tree-model mayhave an order of any one of: two, three, four, five and six.

In some implementations, the method may further comprise displaying thesurvival rate for each available VAD, and identifying the VAD with thehighest survival rate. In certain implementations, the method mayfurther comprise displaying the combination of the selected patientdemographics used for determining the survival rate using abranched-tree representation. The survival rate may comprise aprobability of survival of a patient belonging to the combination ofselected patient demographics when treated with a VAD. In someimplementations, the patient data repository may comprise a AcuteMyocardial Infarction Cardiogenic Shock (AMICS) database or a High-RiskPercutaneous Coronary Interventions (High-Risk PCI) database.

According to a third embodiment of the present disclosure, there isprovided a system for providing a treatment recommendation to aphysician for treating a patient. The system comprises at least oneventricular assist device (VAD) comprising a sensor. The system furthercomprise a processor in communication with the VAD, the processorconfigured to be in communication with an Acute Myocardial InfarctionCardiogenic Shock (AMICS) repository or a High-Risk PercutaneousCoronary Interventions (High-Risk PCI) repository. Further, the systemcomprises a controller in communication with the VAD and the processor,the controller being configured to perform a method according to any ofthe aforementioned embodiments.

According to a fourth embodiment of the present disclosure, there isprovided a system for providing a treatment recommendation to aphysician for treating a patient. The system comprises a processor and acontroller configured to perform a method according to any of theaforementioned embodiments.

According to a fifth embodiment of the present disclosure, there isprovided a computer program comprising computer executable instructions,which, when executed by a computing apparatus comprising a processor anda controller, causes the computing apparatus to perform a methodaccording to any of the aforementioned embodiments.

According to a sixth embodiment of the present disclosure, there isprovided a non-transitory computer-readable storage medium having storedthereon computer-readable code which, when executed by a computingapparatus comprising a processor and a controller, causes the computingapparatus to perform a method according to any of the aforementionedembodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects and advantages will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative system according to an embodiment of thepresent disclosure;

FIG. 2 shows an illustrative flowchart of a method for providing atreatment recommendation to a physician for treating a patient accordingto an embodiment of the present disclosure;

FIGS. 3A-3C show illustrative tree-diagrams with patient demographicfactors that contribute to the survival rate for use of a VAD on apatient;

FIG. 4A illustrates precision of the prediction model based on theorder/depth of the tree diagram of the method of the present disclosure;

FIG. 4B illustrates output of the prediction model with an optimizedtree depth; and

FIG. 5 shows an illustrative flowchart of a method for providing asecond treatment recommendation to a physician for treating a patientaccording to a further embodiment of the present disclosure.

DETAILED DESCRIPTION

To provide an overall understanding of the methods and systems describedherein, certain illustrative embodiments will be described. Although theembodiments and features described herein are specifically described foruse in connection with survival rates for ventricular assist devices, itwill be understood that all the components and other features outlinedbelow may be combined with one another in any suitable manner and may beadapted and applied to other types of medical therapy having survivalrates associated therewith.

The systems and methods described herein use predictive modeling todetermine an optimal treatment recommendations for a patient incardiogenic shock. Treatment recommendations may comprise the use of asingle ventricular assist device (VAD) or a plurality of VADs, incombination with each other. The predictive model pulls in data andstatistics from archived ventricular assist procedures performed in thepast. Such patient data may be stored in a patient data repository suchas an Acute Myocardial Infarction Cardiogenic Shock (AMICS) database ora High-Risk Percutaneous Coronary Interventions (High-Risk PCI)database, for example. The systems and methods use machine learningalgorithms to predict survival rates when treating a patient with a VAD.To further customize the predictive model, patient demographics andoperational parameters of the VAD are also included in the model using aselected tree-based combination of features. Here physicians are able tocombine any number of patient demographics and/or device features toobtain a treatment recommendation with a realistic survival rate.

Additionally, real time patient data from use of a VAD on the patientmay also be fed into the prediction model to further optimize thetreatment recommendation. Such real time patient data may include, butis not limited to, Mean Arterial Pressure (MAP), Left VentricularPressure (LVP), Left Ventricular End-Diastolic Pressure (LVEDP),Pulmonary Arterial Wedge Pressure (PAWP), Pulmonary Capillary WedgePressure (PCWP), and Pulmonary Artery Occlusion Pressure (PAOP). VADsmay comprise, but are not limited to, an Impella® pump, anExtracorporeal Membrane Oxygenation (ECMO) pump, a balloon pump, and aSwan-Ganz catheter. The Impella® pump may comprise an Impella 2.5® pump,an Impella 5.0® pump, an Impella CP® pump and an Impella LD® pump, allof which are by Abiomed, Inc. of Danvers, Mass.

FIG. 1 shows a block diagram of a system 100 for providing a firsttreatment recommendation 110 to a physician for treating a patient 120.The first treatment recommendation 110 comprises an indication to aphysician as to the most appropriate VAD to use in view of the conditionof the patient 120. VADs may comprise, but are not limited to, anImpella® pump, an Extracorporeal Membrane Oxygenation (ECMO) pump, aballoon pump, and a Swan-Ganz catheter. The Impella® pump may comprisean Impella 2.5® pump, an Impella 5.0® pump, an Impella CP® pump, anImpella RP® pump and an Impella LD® pump, all of which are by Abiomed,Inc. of Danvers, Mass. The first treatment recommendation 110 isdetermined using a prediction model based on selected demographicsapplicable to the patient 120 and the operational parameters of all VADssuitable for the patient's condition. Here the survival rates of allVADs are then determined based on the selected demographics of thepatient 120 and the operational parameters of the VADs. The VADassociated with the highest survival rate is then recommended to thephysician. The indication may be by way of a display of the computingunit 130. In relation to FIG. 1, the first recommendation 110 comprisesan indication to the physician to use a first VAD, having a firstsurvival rate (SR1), for treating patient 120.

The system 100 also comprises a computing apparatus 130, such as alaptop, for example, in communication with a patient data repository140. For the sake of brevity only a processor 135 of computing apparatus130 is shown in FIG. 1. However it will be understood that computingapparatus 130 also comprises other components typically associated witha computing apparatus, such as, for example, a volatile memory (e.g. arandom access memory RAM), a non-volatile memory (e.g. a read onlymemory ROM), a display, and connection busses that enable communicationbetween these components, all of which are included in the presentdisclosure.

The computing apparatus 130 comprises a processor 135 that is able toperform operations on data using the prediction model. The computingapparatus 130 is in communication with a patient data repository 140comprising patient data obtained from various medical institutions.According to certain embodiments of the present disclosure, patient datarepository 140 may comprise an Acute Myocardial Infarction CardiogenicShock (AMICS) database or a High-Risk PCI database compiled andmaintained by a CRM such as Salesforce.com, Inc. The patient datarepository 140 stores data from treatment of acute myocardial infarction(AMI) patients, high-risk PCI patients, and patients in cardiogenicshock, for example. Patient data includes patient demographics such as,for example, gender, age and region. Patient data also includes datafrom previous treatments such as, for example, duration of support,indication for use, insertion site, treatment device, and ejectionfraction. Exemplary AMICS data is shown in Table 1. In some instances,the patient data repository 140 also comprises a database 145 ofavailable VADs and their associated operation parameters.

Processor 135 of processing unit 130 is also in communication with acontroller 150 which controls the operation of any VAD used to treat thepatient 120. Each VAD comprises a sensor that collects data from thepatient while the VAD is in use, and transmits this data as signals(such as SIG1 in FIG. 1) to the controller 150. Such data may include,but is not limited to, Mean Arterial Pressure (MAP), Left VentricularPressure (LVP), Left Ventricular End-Diastolic Pressure (LVEDP),Pulmonary Arterial Wedge Pressure (PAWP), Pulmonary Capillary WedgePressure (PCWP), and Pulmonary Artery Occlusion Pressure (PAOP). Thefirst VAD of the first treatment recommendation 110 is connected to thecontroller 150, and transmits a first signal (SIG1) to the controller150. In some embodiments, controller 150 comprises an Automatic Impella®Controller (AIC) by Abiomed, Inc. of Danvers, Mass. Controller 150 maybe housed in a servicing hub 160 that may comprise other components toensure that the respective VADs connected thereto are in operationalorder. In certain embodiments, the processor 130, the processing unit135, the controller 150 and the servicing hub 160 may be housed in awork station 170. The work station 170 may further comprise a display(not shown) to indicate a treatment recommendation to the physician.

TABLE 1 Exemplary AMICS data Ejection Procedure Procedure FractionOutcome Outcome_1 Support Status at End Case Swan-Ganz ECMO Used 1881440 Survived Survived Explanted in Cath Lab Missing Missing 18815

Missing Missing Explanted in Cath Lab Missing Not Used 18816 15 SurvivedSurvived Explanted in Cath Lab Missing Missing 18817

Unknown Unknown Unknown Missing Missing 18818

Survived Survived Explanted in Cath Lab Missing Missing 18819 10Survived Survived On Support/Sent to Unit Missing Missing 18820

Survived Survived Explanted in Cath Lab Missing Missing 18821 10 MissingMissing Expired in Cath Lab Missing Missing 18822 10 Missing MissingExplanted in Cath Lab Missing Missing 18823 10 Missing Missing Explantedin Cath Lab Missing Not Used 18824 15 Missing Missing Explanted in CathLab Missing Not Used 18825 15 Missing Missing Explanted in Cath LabMissing Not Used 18826

Missing Missing Unknown Missing Unknown 18827

Survived Survived On Support/Sent to Unit Missing Missing 18828 15Survived Survived Explanted within 3 Hours of Admission to ICU MissingMissing 18829 35 Missing Missing Explanted in Cath Lab Missing Not Used18830

Missing Missing Explanted in Cath Lab Missing Not Used 18831 20 SurvivedSurvived Explanted within 3 Hours of Admission to ICU Missing Missing18832

Survived Survived Explanted in Cath Lab Missing Missing 18833 10 MissingMissing Explanted in Cath Lab No Not Used 18834

Missing Missing Expired within 3 Hours of Admission to ICU MissingMissing 18835

Survived Survived Explanted in Cath Lab Missing Missing 18836

Missing Missing Explanted within 3 Hours of Admission to ICU MissingMissing 18837  5 Missing Missing Explanted within 3 Hours of Admissionto ICU Missing Not Used 18838

Survived Survived On Support/Sent to Unit Missing ECMO added to impella18839 25 Missing Missing Explanted in Cath Lab Missing Missing

indicates data missing or illegible when filed

According to certain embodiments of the present disclosure, thecontroller 150 may additionally provide the physician with an indicationof a second treatment recommendation after the physician uses the firstVAD to treat the patient 120. Here, the transmitted first signal SIG1from the first VAD is received by the controller 150. The controller 150then determines a second treatment recommendation comprising the use ofat least a second VAD using a comparison model based on SIG1, theoperational parameters of the first VAD, and the operational parametersof all suitable VADs in the database 145. The survival rates of allsuitable VADs is also determined and the VAD with the highest survivalrates is selected as the second VAD. A second survival rate (SR2) of thesecond VAD is also determined by the controller 150 from the AMICSdatabase 140.

The controller 150 then compares the first survival rate SR1 with thatof the second survival rate SR2. If the second survival rate SR2 ishigher than the first survival rate SR1, i.e. SR2>SR1, the secondrecommendation is provided to the physician, via the display on theworkstation 170. The second recommendation may comprise an indication tothe physician to use the second VAD in place of the first VAD to treatthe patient 120. The second recommendation may also comprise anindication to the physician to use a combination of VADs selected fromthe database 145. The VADs used in the combination may comprise VADsother than the first VAD, or at least one second VAD in addition to thefirst VAD. Further, the second recommendation may comprise the use of noVAD at all, i.e. the second recommendation may be an indication to thephysician to stop treating the patient with any VAD. If the secondsurvival rate SR2 is not higher than the first survival rate SR1, i.e.SR2≤SR1, the controller 150 indicates to the physician that no change tothe first treatment recommendation should be made.

FIG. 2 shows a flowchart of a method 200 of providing a first treatmentrecommendation to a physician for treating a patient in cardiogenicshock according to an embodiment of the present disclosure. The method200 is based on the features of the system 100 as described in theforegoing in relation to FIG. 1. The method begins at step 210 in whicha processor 135 of a computing unit 130 accesses a patient datarepository 140. In some embodiments, the repository 140 may comprise anAMICS or High-Risk PCI database which includes a VAD database 145. Theprocessor 135 then determines, in step 220, VADs suitable for treatingthe patient. Such suitability may be based on clinical indications ofthe patient, and the operational parameters of the VAD obtained from thepatient data repository 140. The processor 135 therefore determines ashortlist of suitable VADs from the patient data repository 140 fortreating the patient in step 220.

In step 230 of the method, the processor 135 uses a prediction model todetermine a survival rate for each VAD in the shortlist from step 220.The prediction model is based on a machine learning algorithm, which, inturn includes, but is not limited to, a bagging and random forestalgorithm, a logistic regression algorithm, a classification decisiontree algorithm, a deep learning algorithm, a naïve Bayes algorithm and asupport vector machines algorithm, the details of which are omitted fromthis disclosure for brevity.

For example, the logistic regression algorithm is based on an equationused to represent the predictive model with coefficients learned fromtraining data. A representation of the model may be stored in a memoryof the computing unit 130 as a series of the coefficients, eachcorresponding to a weight indicative of a relative importance of aparticular feature (e.g. a particular patient demographic), and can beused to calculate a probability, which is then translated as thesurvival rate of a patient. The probability may be calculated as(1+exp(−x))⁻¹, wherein x is equal toα*Feature_α+β*Feature_β+γ*Feature_γ+ . . . for any number of features(Feature_α, Feature_β, Feature_γ) and associated coefficients (α, β, γ).

In another example, the decision tree algorithm uses a decision tree asa predictive model to go from observations about an item to conclusionsabout the item's target value. Tree depth may be a hyper-parameter indecision tree learning. A hyper-parameter is a value that cannot beestimated from data used in the model. Hyper-parameters are often usedto help estimate model parameters and can be tuned for a givenpredictive modeling problem. Precision may be used as a performancemetric of a predictive model. By determining the maximum precision ofthe decision tree through tuning hyper-parameters such as tree depth,the system can provide an optimized machine learning model, andtherefore better provide a prediction (such as the survival rate).Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) mayalso be used as metrics to compare prediction algorithms.

In step 240 of the method, the processor 135 compares the survival rateobtained for each VAD and determines the VAD with the highest survivalrate. In step 250, the processor 135 provides a first treatmentrecommendation to the physician, via an indication on the displayconnected to the processor, to use the VAD with the highest survivalrate for treating the patient. The provision of the first treatmentrecommendation in the method 200 of FIG. 2 customizes the firsttreatment recommendation to the needs of the patient using thetree-based data driven protocol as described in the foregoing. By finecustomizing the treatment to the patient's demographics, the mosteffective treatment plan (based on historical analysis of data in datarepository 140, for example) with the best survival rate is provided tothe patient, thereby ensuring effective treatment of patients incardiogenic shock.

FIG. 3A illustrates a patient demographic/feature combination treeaccording to an embodiment of the present disclosure. The tree 300illustrates how various types of features and treatments can be combinedto determine the treatment with the highest predicted survival rate. Thefeatures are determined from the attributes of the data 310 stored inthe data repository 140. As seen from Table 1, the data stored in therepository 140 may have various attributes such as gender, age, ejectionfraction, type of catheter used (e.g. Swan-Ganz), and type of pump used(e.g. ECMO). The attributes of the data that relate to the demographicsof the patient are used in the combination tree 300 as status layerfeatures, such as features 322-325. Status layer features are featuresthat relate to the patient and cannot be changed, i.e. the status layerfeatures do not have adjustable values. Examples of status layerfeatures include, but are not limited to, gender, age and ejectionfraction. The repository 140 also stores data that relates to the typeof VADs available. The features of such VADs are used in the combinationtree 300 as protocol layer features, such as features 326-327. Protocollayer features are physician selectable options of the VAD that arefacilitate controlled operation of the VAD. The protocol layer featureshave values associated with them that can be specified by the physician.Examples of protocol layer features include, but are not limited to,rotor speed and flow rate.

The selected status layer features and selected protocol layer featurespull patient data 310 from the data repository 140, such that saidpulled data can be used in a prediction model to determine and providethe physician with predicted survival rates 328-329 for each respectivecombination of selected features 322-327. The predicted survival rates328-329 are provided in a prediction layer in tree 300. In someembodiments, the prediction model may identify the combination offeatures 322-327 that gives the highest predicted survival rate. Thecombination of features 322-327 that relate to the highest predictedsurvival rate are provided to the physician as a treatmentrecommendation. The treatment recommendation may be provided to thephysician on a monitor. The combination of features that make up thetreatment recommendation may be presented to the physician in a featurecombination tree, such as tree 310 in FIG. 3A.

It will be understood that cardiac assist technologies develop overtime. Further, increased exposure and use of VADs by physicians improvetheir impact on a patient's condition (e.g. once a physician is bettertrained at using a VAD, the effect the use of the VAD has on a patientwith a particular condition would be seen in the patient data—forexample the ejection fraction for a particular VAD may increase).According to an embodiment of the present disclosure, in order to caterto such factors, the prediction model may specify a date range whenpulling patient data 310 from the data repository 140. In suchsituations, the prediction model effectively weighs the data and onlyuses patient data 310 from the repository 140 that falls within thespecified date range. While such data weightage is exemplified in theforgoing by way of a date range, other factors may be considered whenweighing the pulled data 310.

FIG. 3B illustrates an exemplary feature combination tree 360 of varioustypes of patient demographic data used in the predictive model accordingto an embodiment of the present disclosure. As previously described, thepatient demographic data is stored in the patient data repository 140and may comprise, for example, gender, age and region. Tree 360illustrates the combination of the patient's gender 362 and age 363,364. These combinations are for the use of a specific type of VAD (e.g.Impella® 2.5 pump, for example). Based on data extracted from thepatient data repository 140, feature 362 has values ‘male’ and ‘female’,and features 363, 364 have values ‘50-59’ and ‘60-69’. Thus based on thetree-based data combination illustrated in FIG. 3B, using the data inthe patient data repository 140 for the selected patient demographicdata, and a machine learning algorithm (as detailed above), the survivalrates 365-368 for each combination of the tree 360 is as illustrated inFIG. 3B. FIG. 3B shows that male patients in the age range of 60-69, inwhich a particular VAD has been implanted (e.g. an Impella® 2.5 pump),have the highest survival rate of 84.86%. The data used for determiningthe survival rate in FIG. 3B is based on 1,071+1,697+1,307+2,317=6,392records in the patient data repository 140 which have features 362-364.The example in FIG. 3B illustrates the influence of the combination offeatures of patient data on the survival rate as determined by theprediction model.

A further example of the tree-based data-driven method of the presentdisclosure is illustrated in the feature combination tree 370 shown inFIG. 3C. Tree 370 illustrates the combination of three types of patientdata: gender 372, ejection fraction 374, and the availability ofhemodynamic monitoring 376. The availability of hemodynamic monitoringis dependent on the type of VAD used. For example Swan-Ganz cathetersare known to have pressure sensors available that facilitate suchhemodynamic monitoring. The selected patient demographics for thecombination in FIG. 3C are: male, ejection fraction of less than 30%,and hemodynamic monitoring. Based on the tree-based data combinationillustrated in FIG. 3C, using the data in the patient data repository140 for the selected patient demographic data, and a prediction model(as detailed above), the survival rates 378-379 for this specificcombination of patient demographics is as illustrated in FIG. 3C. Thehighest survival rate is 58.43% for the use of VADs capable ofhemodynamic monitoring on male patients with an ejection fraction ofless than 30%. The treatment recommendation to the physician istherefore: use a VAD capable of hemodynamic monitoring (e.g. a Swan-Ganzcatheter) for male patients with for an ejection fraction of less than30%. For completeness, branch 378 in the tree 370 indicates the survivalrate for using a VAD that is not capable of hemodynamic monitoring on amale patient for an ejection fraction of less than 30%. The data usedfor determining the survival rate in FIG. 3C is based on 584+421=1,005records in the patient data repository 140 which have features 372, 374and 376. A graphical representation of feature combination trees 360 and370 may also be displayed to the physician alongside the first and/orsecond treatment recommendation, on a monitor, for example.

FIG. 4A illustrates a data plot 400 for precision optimization of theprediction model using a decision tree algorithm. As previouslydescribed, the decision tree algorithm may use a hyper-parameter as thetree depth. By determining the maximum precision of the decision treealgorithm through tuning hyper-parameters such as tree depth, the systemcan provide an optimized machine learning model, and therefore betterprovide a prediction (such as the survival rate). As shown in FIG. 4A,when we change the hyper-parameter (tree depth) to a large value, thedecision tree algorithm is able to capture all the noise of the trainingdata. This results in a high training score, as can be seen by the lineplot 410 in FIG. 4A. However at such large tree depths, the modeloverfits the data and is not generalized enough. As a consequence, thecross-validation score deteriorates, as can be seen by the line plot 420in FIG. 4A. For the decision tree algorithm, if the tree is too shallow,e.g. a tree depth of 2 or 3, the prediction model is too simple and isunable to make any prediction that are correct, as shown in FIG. 4Awhere both the training and cross-validation scores are low. Thus theoptimal tree depth for the decision tree algorithm is one whichmaximizes the cross-validation score and the training score. This occursat the peak of the cross-validation curve (point 430), at a tree depthof 6 as shown in FIG. 4A.

In machine learning, a false positive is a machine indicated resultwhich incorrectly indicates that a condition or attribute is present.Similarly a false negative is one in which the machine indicated resultincorrectly identifies that a condition or attribute is absent. Ideallythe prediction model should predict a survival rate for a VAD thatmatches up with the real survival rate when using the VAD on a patient.However, as with most machine learning, there is no perfect model inreal life and so when evaluating machine learning models, one trades offfalse positive with false negative, and vice versa. The ROC curve for atree depth of 6 is shown in FIG. 4B. The ROC curve 450 scans throughfalse positive rate from 0 to 100% and checks what is the true positiverate given by the model. The line plot 460 is the ideal with an areaunder curve AUC of 100%, while the line plot 470 is based on a randommodel with an AUC of 50%. Any reasonable prediction model should stay inbetween line plot 460 and line plot 470. For the decision tree algorithmdiscussed above in relation for FIG. 4A, the true positive rate is shownby line plot 480 and the AUC is 87.4% showing a good predictive power ofthe prediction model when using a decision tree algorithm. For clarity,the decision tree algorithm is one of many machine learning algorithmsthat can be used as the prediction model according to the presentdisclosure. The feature combination trees in FIGS. 3B and 3C areseparate from the prediction model and provides the physician with anillustration of how selected features and/or patient demographics arecombined in the decision model.

FIG. 5 shows a flowchart of a method 500 of providing a second treatmentrecommendation to a physician for treating a patient in cardiogenicshock according to a further embodiment of the present disclosure. Themethod 500 is based on the features of the system 100 as described inthe foregoing in relation to FIG. 1. The method 500 works off step 250of method 200 in FIG. 2 in which the physician is provided with a firsttreatment recommendation comprising the use of a first VAD having afirst survival rate (SR1). Method 500 begins at step 510 in which thefirst treatment recommendation is determined by the processor 135. Aspreviously mentioned in relation to FIG. 2, the processor 135 accessesthe patient data repository 140 and determines VADs suitable fortreating the patient. Such suitability may be based on clinicalindications of the patient, and the operational parameters of the VADobtained from patient data repository 140, for example. The processor135 determines a first shortlist of suitable VADs from the patient datarepository 140 for treating the patient and uses a prediction model todetermine a survival rate for each VAD in the first shortlist. Aspreviously mentioned, the prediction model is based on a machinelearning algorithm, which, in turn includes, but is not limited to, abagging and random forest algorithm, a logistic regression algorithm, aclassification tree algorithm, a deep learning algorithm, a naïve Bayesalgorithm and a support vector machines algorithm. The VAD with thehighest survival rate is used for the first treatment recommendation,termed the first VAD having a first survival rate (SR1).

The physician is informed of the first treatment recommendation via adisplay unit connected to the processor 135, and uses the first VAD totreat the patient (step 520). The feature combination tree (e.g. tree300 and 350) may also be displayed. Each VAD comprises a sensor thatcollects data from the patient while the VAD is in use (step 530), andtransmits this data as signals (SIG1) to the controller 150 and theprocessor 135. In some embodiments of the present disclosure, thecollection of patient data or patient vitals, termed a patient vitalscheck, is done at predetermined intervals of time during the period inwhich the patient is being treated with the first VAD. As previouslymentioned, such data may include, but is not limited to, Mean ArterialPressure (MAP), Left Ventricular Pressure (LVP), Left VentricularEnd-Diastolic Pressure (LVEDP), Pulmonary Arterial Wedge Pressure(PAWP), Pulmonary Capillary Wedge Pressure (PCWP), and Pulmonary ArteryOcclusion Pressure (PAOP).

In step 540, the processor 135 determines a second shortlist of VADsfrom the patient data repository 140 that are suitable in treating thepatient based on the patient data contained in SIG1. As will beappreciated, as the patient is treated with the first VAD, the patient'svitals may change and therefore the patient data in SIG1 may bedifferent to the clinical indications of the patient used in determiningthe first treatment recommendation. Thus the VADs in the secondshortlist may be different to those in the first shortlist. As with theVADs in the first shortlist, the processor 135 uses a prediction modelto determine a survival rate for each VAD in the second shortlist. Theprediction model is based on a machine learning algorithm, which mayinclude but is not limited to, a bagging and random forest algorithm, alogistic regression algorithm, a classification and regression treealgorithm, a deep learning algorithm, a decision tree algorithm, a naïveBayes algorithm, a support vector machines algorithm and a vectorquantization algorithm.

The VAD with the highest survival rate, termed the second VAD, isdetermined (step 540) and the processor 135 compares its survival rate(SR2) to that of the first VAD (SR1), in step 550. If SR2>SR1, thesecond VAD is used in a second treatment recommendation to the physician(step 560). The physician is informed of the second treatmentrecommendation via a display unit connected to the processor 135. IfSR2≤SR1, the second treatment recommendation is not provided to thephysician, and, instead, an indication is made to the physician (via themonitor) to continue using the first treatment recommendation andcontinue treating the patient with the first VAD, step 520, until thenext patient vitals check. After the second treatment recommendation isprovided to the physician, the method 500 may continue to performpatient vitals checks to further refine the treatment process, as shownin FIG. 5.

The provision of the second treatment recommendation in the method 500of FIG. 5 further customizes the first treatment recommendation to theprogress of the patient undergoing said treatment using the tree-baseddata driven protocol as described in the foregoing. Such furthercustomization ensures that the treatment recommendation with the highestsurvival rate is provided to the physician for the further treatment ofpatient. By fine turning the treatment to the progress of the patient,the treatment of patients in cardiogenic shock will be more effective.

In certain embodiments, feature combination trees are also displayed ona monitor attached to the processor running the decision model. Thesefeature combination trees are similar to those depicted in FIGS. 3B and3C. Such feature combination trees provide the physician with avisualization of the features that have an influence on the recommendedVAD for treating the patient.

In certain embodiments, the second treatment recommendation may comprisethe use of a single second VAD or a plurality of second VADs, incombination with each other. For example, the first treatmentrecommendation may comprise the use of a balloon pump while the secondtreatment recommendation may comprise the use of an ECMO pump incombination with a Swan-Ganz catheter. As a further example, the firsttreatment recommendation may comprise the use of an Impella 2.5® pumpwhile the second treatment recommendation may comprise the continued useof the Impella 2.5® pump in combination with a balloon pump. In suchsituations, the second shortlist will generate two VADs associated withthe highest survival rates. In certain embodiments of the presentdisclosure, the processor can be configured to ascertain the top n VADswith the higher survival rate, where n≥1. The survival rates of these nVADs are then compared to that of the first VAD in step 550 of method500 in FIG. 5. According to some embodiments of the present disclosure,the second treatment recommendation may be to stop the use of any VAD,as in the ‘No’ option 391 in FIG. 3, whereby the ‘No VAD’ option for afirst VAD gives the highest survival rate.

In relation to the present disclosure, a computer-readable medium maycomprise a computer-readable storage medium that may be any tangiblemedia or means that can contain or store the instructions for use by orin connection with an instruction execution system, apparatus, ordevice, such as a computer as defined previously, for performing any ofthe methods described herewith.

According to various embodiments of the present disclosure, a computerprogram may be implemented in a computer program product comprising atangible computer-readable medium bearing computer program code embodiedtherein which can be used with the processor for the implementation ofthe functions or methods described above.

Reference to “computer-readable storage medium”, “computer programproduct”, “tangibly embodied computer program” etc., or a “processor” or“processing circuit” etc. should be understood to encompass not onlycomputers having differing architectures such as single/multi-processorarchitectures and sequencers/parallel architectures, but alsospecialized circuits such as field programmable gate arrays FPGA,application specify circuits ASIC, signal processing devices and otherdevices. References to computer program, instructions, code etc. shouldbe understood to express software for a programmable processor firmwaresuch as the programmable content of a hardware device as instructionsfor a processor or configured or configuration settings for a fixedfunction device, gate array, programmable logic device, etc.

By way of example, and not limitation, such “computer-readable storagemedium” may mean a non-transitory computer-readable storage medium whichmay comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage, or other magnetic storage devices, flash memory,or any other medium that can be used to store desired program code inthe form of instructions or data structures and that can be accessed bya computer. Also, any connection is properly termed a “computer-readablemedium”. For example, if instructions are transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.It should be understood, however, that “computer-readable storagemedium” and data storage media do not include connections, carrierwaves, signals, or other transient media, but are instead directed tonon-transient, tangible storage media. Disk and disc, as used herein,include compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-ray disc, where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope of“computer-readable medium”.

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor,” as used herein may referto any of the foregoing structure or any other structure suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated hardware and/or software modules. Also, the techniques couldbe fully implemented in one or more circuits or logic elements.

If desired, the different steps discussed herein may be performed in adifferent order and/or concurrently with each other. Furthermore, ifdesired, one or more of the above described steps may be optional or maybe combined.

The foregoing is merely illustrative of the principles of thedisclosure, and the apparatuses can be practiced by other than thedescribed implementations, which are presented for purposes ofillustration and not of limitation. It is to be understood that themethods disclosed herein, while shown for use in automated ventricularassistance systems, may be applied to systems to be used in otherautomated medical systems.

Variations and modifications will occur to those of skill in the artafter reviewing this disclosure. The disclosed features may beimplemented, in any combination and subcombination (including multipledependent combinations and subcombinations), with one or more otherfeatures described herein. The various features described or illustratedabove, including any components thereof, may be combined or integratedin other systems. Moreover, certain features may be omitted or notimplemented.

Examples of changes, substitutions, and alterations are ascertainable byone skilled in the art and could be made without departing from thescope of the information disclosed herein. All references cited hereinare incorporated by reference in their entirety and made part of thisapplication.

1. A method for providing a treatment recommendation to a physician fortreating a patient, the method comprising: determining, from a processorin communication with a patient data repository, a first treatmentrecommendation based on a combination of selected patient demographicsfrom the patient data repository applicable to the patient andoperational parameters of a plurality of ventricular assist devices(VADs) suitable for treating the patient, the first treatmentrecommendation having a first survival rate and comprising the use of afirst VAD; using the first VAD to treat the patient; obtaining, from acontroller, a first signal; determining, by the processor, a secondtreatment recommendation based on the first signal and the firsttreatment recommendation, the second treatment recommendation having asecond survival rate; and providing, by the processor, the secondtreatment recommendation to the physician if the second survival rate ishigher than the first survival rate.
 2. The method of claim 1, furthercomprising informing the physician to continue using the first VAD totreat the patient if the second survival rate is not higher than thefirst survival rate.
 3. The method of claim 1, wherein each VADcomprises at least one sensor for providing the first signal to thecontroller.
 4. The method of any of claim 1, wherein the first signalcomprises information concerning the patient's vitals.
 5. The method ofany of claim 1, wherein the first signal comprises at least one of: MeanArterial Pressure (MAP), Left Ventricular Pressure (LVP), LeftVentricular End-Diastolic Pressure (LVEDP), Pulmonary Arterial WedgePressure (PAWP), Pulmonary Capillary Wedge Pressure (PCWP), PulmonaryArtery Occlusion Pressure (PAOP).
 6. The method of claim 1, furthercomprising treating the patient with the second treatmentrecommendation.
 7. The method of claim 1, wherein the VAD comprises atleast one of: an Impella® pump, an Extracorporeal Membrane Oxygenation(ECMO) pump, a balloon pump, and a Swan-Ganz catheter.
 8. The method ofclaim 7, wherein the Impella® pump comprises any one of: Impella 2.5®pump, an Impella 5.0® pump, an Impella CP® pump, an Impella RP® pump andan Impella LD® pump.
 9. The method of claim 1, wherein the secondtreatment recommendation comprises continuing use of the first VAD fromthe first treatment recommendation in addition to a second VAD.
 10. Themethod of claim 1, wherein the second treatment recommendation comprisesthe use of at least one of the following for treating the patient: thefirst VAD, a second VAD, and no VAD.
 11. The method of claim 1, whereinthe patient is in cardiogenic shock.
 12. The method of claim 1, whereinthe first treatment recommendation is determined by a prediction modelexecuted by the processor.
 13. The method of claim 12, wherein theprediction model is based on a machine learning algorithm comprising anyone of: a bagging and random forest algorithm, a logistic regressionalgorithm, a classification decision tree algorithm, a deep learningalgorithm, a naïve Bayes algorithm, and a support vector machinesalgorithm.
 14. The method of claim 1, wherein the types of patientdemographics comprise: gender, age, region, duration of support,indication for use and insertion site.
 15. The method of claim 1,further comprising: displaying the survival rate for each available VAD;and identifying the VAD with the highest survival rate.
 16. The methodof claim 1, further comprising: displaying the combination of theselected patient demographics used for determining the survival rateusing a branched-tree representation.
 17. The method of claim 1, whereinthe survival rate comprises a probability of survival of a patientbelonging to the combination of selected patient demographics whentreated with a VAD.
 18. The method of claim 1, wherein the patient datarepository comprises a Acute Myocardial Infarction Cardiogenic Shock(AMICS) database or a High-Risk Percutaneous Coronary Interventions(High-Risk PCI) database.
 19. (canceled)
 20. (canceled)
 21. (canceled)22. (canceled)
 23. A method for providing a treatment recommendation toa physician for treating a patient, the method comprising: obtaining, bya processor, patient data from a patient data repository, the datastored in the repository according to patient demographics; determining,by the processor, at least one ventricular assist device (VAD) suitablefor treating patient; determining, by the processor using a predictionmodel, a survival rate for each suitable VAD based on data from thepatient data repository for a combination of selected patientdemographics applicable to the patient; providing, to a controller, arecommended first VAD associated with the highest survival rate; andusing, by the physician, the recommended first VAD to treat the patient.24. The method of claim 23, further comprising: providing the physicianwith the survival rate for each suitable VAD for all combinations of theselected patient demographics applicable to the patient.
 25. The methodof claim 23, further comprising: providing the physician with a survivalrate for not using a VAD for each combination of the selected patientdemographics applicable to the patient.
 26. The method of claim 23,wherein the first VAD comprises at least one of: an Impella® pump, anExtracorporeal Membrane Oxygenation (ECMO) pump, a balloon pump, and aSwan-Ganz catheter.
 27. The method of claim 26, wherein the Impella®pump comprises any one of: Impella 2.5® pump, an Impella 5.0® pump, anImpella CP® pump, an Impella RP® pump and an Impella LD® pump.
 28. Themethod of claim 23, wherein the patient demographics comprise: age,gender, region, year of implantation, support device, duration ofsupport, insertion site and ejection fraction.
 29. The method of claim23, wherein the prediction model uses a machine learning algorithm todetermine the survival rate.
 30. The method of claim 23, wherein themachine learning algorithm comprises any one of: a bagging and randomforest algorithm, a logistic regression algorithm, a classificationdecision tree algorithm, a deep learning algorithm, a naïve Bayesalgorithm, and a support vector machines algorithm.
 31. The method ofclaim 23, wherein the combination of the selected patient demographicsfollows a tree-model.
 32. The method of claim 31, wherein the tree-modelhas an order of any one of: two, three, four, five and six.
 33. Themethod of claim 23, further comprising treating the patient using thefirst treatment recommendation.
 34. The method of claim 23, furthercomprising: displaying the survival rate for each available VAD; andidentifying the VAD with the highest survival rate.
 35. The method ofclaim 23, further comprising: displaying the combination of the selectedpatient demographics used for determining the survival rate using abranched-tree representation.
 36. The method of claim 23, wherein thesurvival rate comprises a probability of survival of a patient belongingto the combination of selected patient demographics when treated with aVAD.
 37. The method of claim 23, wherein the patient data repositorycomprises a Acute Myocardial Infarction Cardiogenic Shock (AMICS)database or a High-Risk Percutaneous Coronary Interventions (High-RiskPCI) database.
 38. (canceled)
 39. (canceled)
 40. (canceled) 41.(canceled)